{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## _*H2 plot with different basis sets used*_\n",
    "\n",
    "This notebook demonstrates using Qiskit Chemistry to plot graphs of the ground state energy of the Hydrogen (H2) molecule over a range of inter-atomic distances in different basis sets.\n",
    "\n",
    "This notebook has been written to use the PSI4 chemistry driver. See the PSI4 chemistry driver readme if you need to install the external Psi4 program that this driver requires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing step 20 --- complete\n",
      "Distances:  [0.5   0.525 0.55  0.575 0.6   0.625 0.65  0.675 0.7   0.725 0.75  0.775\n",
      " 0.8   0.825 0.85  0.875 0.9   0.925 0.95  0.975 1.   ]\n",
      "Energies: [[-1.05515979 -1.07591366 -1.09262991 -1.10591805 -1.11628601 -1.12416092\n",
      "  -1.12990478 -1.13382622 -1.13618945 -1.13722138 -1.13711707 -1.13604436\n",
      "  -1.13414767 -1.13155121 -1.12836188 -1.12467175 -1.12056028 -1.11609624\n",
      "  -1.11133942 -1.10634211 -1.10115033]\n",
      " [-1.07121344 -1.09011427 -1.10532121 -1.11742954 -1.12693056 -1.13423167\n",
      "  -1.13967222 -1.14353615 -1.14606218 -1.14745209 -1.14787738 -1.14748463\n",
      "  -1.14639978 -1.14473155 -1.14257409 -1.14000911 -1.13710763 -1.13393134\n",
      "  -1.13053375 -1.12696114 -1.1232535 ]\n",
      " [-1.0778639  -1.09627049 -1.111046   -1.12277894 -1.13195346 -1.13897049\n",
      "  -1.14416409 -1.14781424 -1.15015683 -1.15139164 -1.15168855 -1.15119257\n",
      "  -1.15002788 -1.14830105 -1.14610373 -1.14351485 -1.14060245 -1.13742526\n",
      "  -1.13403397 -1.13047239 -1.12677835]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab\n",
    "from qiskit.chemistry.drivers import PSI4Driver\n",
    "from qiskit.aqua.algorithms import NumPyMinimumEigensolver\n",
    "from qiskit.chemistry.core import Hamiltonian\n",
    "\n",
    "# PSI4 config here is a multi-line string that we update using format()\n",
    "# To do so all other curly brackets that are required in the PSI4 config must be doubled\n",
    "psi4_cfg = \"\"\"\n",
    "molecule h2 {{\n",
    "   0 1\n",
    "   H 0.0 0.0 -{0}\n",
    "   H 0.0 0.0 {0}\n",
    "}}\n",
    "\n",
    "set {{\n",
    "  basis {1}\n",
    "  scf_type pk\n",
    "}}\n",
    "\"\"\"\n",
    "basis_sets = ['sto-3g', '3-21g', '6-31g']\n",
    "start = 0.5  # Start distance\n",
    "by    = 0.5  # How much to increase distance by\n",
    "steps = 20   # Number of steps to increase by\n",
    "energies  = np.empty([len(basis_sets), steps+1])\n",
    "distances = np.empty(steps+1)\n",
    "\n",
    "print('Processing step __', end='')\n",
    "for i in range(steps+1):\n",
    "    print('\\b\\b{:2d}'.format(i), end='', flush=True)\n",
    "    d = start + i*by/steps\n",
    "    for j in range(len(basis_sets)):\n",
    "        driver = PSI4Driver(psi4_cfg.format(d/2, basis_sets[j]))\n",
    "        qmolecule = driver.run()\n",
    "        operator =  Hamiltonian()\n",
    "        qubit_op, aux_ops = operator.run(qmolecule)\n",
    "        result = NumPyMinimumEigensolver(qubit_op).run()\n",
    "        result = operator.process_algorithm_result(result)\n",
    "        energies[j][i] = result.energy\n",
    "    distances[i] = d\n",
    "print(' --- complete')\n",
    "\n",
    "print('Distances: ', distances)\n",
    "print('Energies:', energies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for j in range(len(basis_sets)):\n",
    "    pylab.plot(distances, energies[j], label=basis_sets[j])\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('H2 Ground State Energy in different basis sets')\n",
    "pylab.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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